Bibliographic record
Abstract
ABSTRACT We investigate the determinants and consequences of budget reallocations—that is, corrective changes to the budget made during the year. Using proprietary data from a large consumer goods manufacturer, we analyze the extent to which initial budgeting decisions drive reallocations. Examining this relationship is important because initial budget negotiations are often troubled by power struggles and politicking, which may give rise to the need for reallocations. We hypothesize that one important driver of reallocation decisions is the firm's aim to correct systematic deviations from the optimal initial budget that were driven by lobbying during the initial budgeting process. We find evidence that is consistent with this prediction. In a more exploratory analysis, we show that reallocations do not have the desired effects on market performance. In particular, budget cuts are negatively associated with a product's change in market share. More surprisingly, while budget increases do help product lines achieve their sales targets in the last quarter, they do not boost market share. Our results demonstrate that efficient investment planning is essential to achieve an improvement in market performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".